LOST SALES REDUCTION AND QUALITY IMPROVEMENT WITH VARIABLE LEAD TIME AND FUZZY COSTS IN AN IMPERFECT PRODUCTION SYSTEM
被引:20
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作者:
Soni, Hardik N.
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机构:
Chimanbhai Patel Post Grad Inst Comp Applicat, Ahmadabad 380015, Gujarat, IndiaChimanbhai Patel Post Grad Inst Comp Applicat, Ahmadabad 380015, Gujarat, India
Soni, Hardik N.
[1
]
Sarkar, Biswajit
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机构:
Hanyang Univ, Dept Ind & Management Engn, Ansan 380015, Gyeonggi Do, South KoreaChimanbhai Patel Post Grad Inst Comp Applicat, Ahmadabad 380015, Gujarat, India
Sarkar, Biswajit
[2
]
Mahapatra, Amalendu Singha
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机构:
Techno India Coll Technol, Dept Basic Sci & Humanities, Kolkata 700156, IndiaChimanbhai Patel Post Grad Inst Comp Applicat, Ahmadabad 380015, Gujarat, India
Mahapatra, Amalendu Singha
[3
]
Mazumder, S. K.
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机构:
Indian Inst Engn Sci & Technol, Dept Math, Sibpur 711103, Howrah, IndiaChimanbhai Patel Post Grad Inst Comp Applicat, Ahmadabad 380015, Gujarat, India
Mazumder, S. K.
[4
]
机构:
[1] Chimanbhai Patel Post Grad Inst Comp Applicat, Ahmadabad 380015, Gujarat, India
[2] Hanyang Univ, Dept Ind & Management Engn, Ansan 380015, Gyeonggi Do, South Korea
[3] Techno India Coll Technol, Dept Basic Sci & Humanities, Kolkata 700156, India
[4] Indian Inst Engn Sci & Technol, Dept Math, Sibpur 711103, Howrah, India
Lost sales reduction;
quality improvement;
controllable lead time;
mixtures of distributions;
fuzzy costs;
MIXTURE INVENTORY MODEL;
SETUP COST;
SERVICE LEVEL;
DEMAND;
BACKORDERS;
POLICY;
DELAY;
D O I:
10.1051/ro/2016075
中图分类号:
C93 [管理学];
O22 [运筹学];
学科分类号:
070105 ;
12 ;
1201 ;
1202 ;
120202 ;
摘要:
This article investigates the effects of lost sales reduction and quality improvement in an imperfect production process under imprecise environment with simultaneously optimizing reorder point, order quantity, and lead time. This study assumes that the demand during lead time follows a mixture of normal distributions and the cost components are imprecise and vague. Under these assumptions, the aim is to study the lost sales reduction and the quality improvement in an uncertainty environment. The objective function in fuzzy sense is defuzzified using Modified Graded Mean Integration Representation Method (MGMIRM). For the defuzzified objective function, theoretical results are developed to establish optimal policies. Finally, some numerical examples and sensitivity analysis are provided to examine the effects of non-stochastic uncertainty.